Microsensor systems for cell metabolism - from 2D culture to organ-on-chip (2019-2024).

Cell cultures, organs-on-chip and microphysiological systems become increasingly relevant as models, , in drug development, disease modelling, toxicology or cancer research. It has been underlined repeatedly that culture conditions and metabolic cues have a strong or even essential influence on the reproducibility and validity of such experiments but are often not appropriately measured or controlled. Here we review microsensor systems for cell metabolism for the continuous measurement of culture conditions in microfluidic and lab-on-chip platforms. We identify building blocks, features and essential advantages to underline the relevance of these systems and to derive appropriate requirements for development and practical use. We discuss different formats and geometries of cell culture, microfluidics and the resulting consequences for sensor placement, as the prerequisite for understanding the various approaches and classification of the systems. The major chemical and biosensors based on electrochemical and optical principles are discussed for general understanding and to contextualize current developments. We then review selected recent sensor systems with real-world implementations of sensing in cell cultures and organs-on-chip, employing a helpful characterization. That includes formats and cell models, microfluidic systems and sensor types applied in static and dynamic monitoring of 2D and 3D cell cultures, as well as single spheroids. We discuss notable advances, particularly with respect to sensor performance and the demonstration of long-term continuous measurements. We outline current approaches to system fabrication technologies, material choice, and interfacing, and comment on recent trends. Finally, we conclude with critical remarks on the current state of sensors in cell culture monitoring and identify avenues for future improvements for both developers and users of such systems, which will lead to better and more predictive models.